SISTEM DATA LOGGER SHMS BERBASIS LVDT DENGAN KALMAN FILTER UNTUK PENGUKURAN DISPLACMENT

Abstract

This study presents the development of a prototype Structural Health Monitoring System (SHMS) aimed at measuring displacement in elastomeric bridge bearings using a Linear Variable Differential Transformer (LVDT) sensor integrated with a Kalman Filter. The system is designed to enable accurate, real-time, and reliable monitoring by mitigating signal noise commonly found in LVDT outputs. The prototype is composed of two primary components: a Data Acquisition Unit (DAQ) that reads displacement and transmits data via the MQTT protocol, and a Data Logger that processes the incoming signal using the Kalman Filter, stores it in CSV format, and displays real-time data through a web-based dashboard. The research methodology includes a literature review, mathematical modeling of the Kalman Filter, hardware design, and performance testing in a controlled laboratory environment. Experimental results demonstrate that a Kalman Filter configuration with parameters Q=1e-5 and R=0.01 significantly reduces measurement noise, yielding smoother displacement curves that closely align with micrometer reference data. The findings confirm the effectiveness of the system for monitoring structural displacement. The integration of LVDT sensors, IoT-based communication, and signal processing techniques in this prototype highlights its potential for practical implementation in infrastructure safety assessment.